preprints.org > computer science and mathematics > artificial intelligence and machine learning > doi: 10.20944/preprints202407.2287.v1 (registering DOI)

Preprint Review Version 1 This version is not peer-reviewed

Version 1 : Received: 26 July 2024 / Approved: 29 July 2024 / Online: 29 July 2024 (10:18:24 CEST)

The different systems in the nuclear power plants (NPP) are critical and complex which requires a continuous rigorous monitoring for both normal and abnormal conditions. However, due to the nonlinearity of the dynamic behavior of these systems, implementation of artificial intelligence within the NPP components is crucial to enhance the monitoring and the predictability of the key operating parameters trend. On the other hand, lessons learned from large nuclear accident proves that a remote real-time coordination between the different stakeholders involved in the safety of the nuclear reactor is needed. This remote feature can be implemented in the existing power plants by embedding a mobile computing networks within the plant components. This network will send early warning and transmit the NPP data on real-time to different authorities such as the regulatory authority and the decision-making committees, to enhance the interpretability of the nuclear event and deliver a collaborative decision which in return mitigate the risk associated with these events and increase the overall safety of the plant. The integration of AI and mobile computing in NPP would be most of interest in countries where nuclear reactors data are challenged to reach during an accident. For instance, during the meltdown of Chernobyl, the Soviet government covered the accident from the news in an attempt to contain the consequences but there was a spread of radioactive contamination to some other Europe countries. Therefore, in the case of such scenario the implementation of mobile computing and AI would result in different outcomes as the European countries would be notified through the mobile networking and the spread of contamination would be forecasted by the AI algorithms. In this paper, we examine the different modern AI and mobile computing technologies, the different potential application, the associated features and challenges, and future work direction.

 artificial intelligence (AI); machine learning (ML); mobile computing; nuclear power plant (NPP); internet of thing (IoT); cloud computing; prediction algorithms; cybersecurity; data collection; safety and reliability 

Computer Science and Mathematics, Artificial Intelligence and Machine Learning

* All users must log in before leaving a comment